Beyond the profile: adventures in machine learning and automation

A bit about data science…

• include a couple of charts about data maturity cycle

Profiles

• There are currently 67 profiles with 295 domains
• 1662 unique indicators
• The most frequently used indicators are Index of multiple deprivation score (IMD 2015), % of women who smoke at time of delivery, Prevalence of overweight (including obese) among children in Reception, Prevalence of overweight (including obese) among children in Year 6 , Rate of conceptions per 1,000 females aged 15-17 , Rate of chlamydia detection per 100,000 young people aged 15 to 24, 2.02i - % of all mothers who breastfeed their babies in the first 48hrs after delivery, Smoking Prevalence in adults - current smokers (APS), 3.03x - % of eligible children who have received two doses of MMR vaccine on or after their 1st birthday and at any time up to their 5th birthday, 1.02i - School Readiness: all children achieving a good level of development at the end of reception as a percentage of all eligible children., 3.03xii - Population vaccination coverage for one dose (females 12-13 years old) - HPV , Hospital admissions for alcohol-specific conditions, under 18s, crude rate per 100,000 population.
• That is ~ 56600 spine charts
• (Not including the ~ 120000 spine charts in practice profiles)

Profiles

What if I asked you…?

• Within in any one profile or domain
  • How similar are the spine charts?
  • Can we group local authorities on the basis of their profile?
  • What stories do the data tell?

• Even the “skinniest” domain has at least 5 indicators - some have 50+
• This is complex multivariate data

Automation

• Shiny document
  • https://jflowers.shinyapps.io/clusters_gp/
• For loop
• Parameterisation
• R Markdown

Fingertips API and fingertipsR

• APIs = Application Programming Interface
• Websites for computers
• Fingertips API

Fingertips metadata

• http://rpubs.com/jflowers/304699

fingertipsR

Machine learning

• 2 types
  • Supervised
  • Unsupervised
• Train, test, validate
  
• 5 questions
  • How much?
  • Is it A or B (classification)
  • Is it weird? (outliers and anomalies)
  • Are there patterns in the data (clustering = unsupervised ML)
  • So what - what next?
• Hundreds of algorithms

Diabetes data

Correlations

Correlation network

Clustering

What (if anything) is different about North Essex

kmeans

Data dimensions - dimensionality reduction